Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.

ABSTRACT:

Objective

Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram (ECG) analysis and provide recommendations for selection of explanations methods.

Materials and methods

A residual deep neural network was trained on ECGs to predict intervals and amplitudes. Nine commonly used explanation methods (Saliency, Deconvolution, Guided backpropagation, Gradient SHAP, SmoothGrad, Input × gradient, DeepLIFT, Integrated gradients, GradCAM) were qualitatively evaluated by medical experts and objectively evaluated using a perturbation-based method.

Results

No single explanation method consistently outperformed the other methods, but some methods were clearly inferior. We found considerable disagreem

SUBMITTER: Storas AM 

PROVIDER: S-EPMC11648713 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature